piglot.optimisers.query.QueryOptimiser

class QueryOptimiser(objective: Objective, param_list_file: str, reference_point: list[float] | None = None, nadir_scale: float = 0.1)[source]

Bases: Optimiser

Query optimiser.

Methods

`optimise`	Optimiser for the outside world.
`update_mo_data`	Get the partitioning of the observations in multi-objective optimisation.

optimise(n_iter: int, parameters: ~piglot.parameter.ParameterSet, output_dir: str, stop_criteria: ~piglot.optimiser.StoppingCriteria = <piglot.optimiser.StoppingCriteria object>, verbose: bool = True) → Tuple[float, ndarray]

Optimiser for the outside world.

Parameters

objectiveObjective: Objective function to optimise.
n_iterint: Maximum number of iterations.
parametersParameterSet: Set of parameters to optimise.
output_dirstr: Whether to write output to the output directory, by default None.
stop_criteriaStoppingCriteria: List of stopping criteria, by default none attributed.
verbosebool: Whether to output progress status, by default True.

Returns

float: Best observed objective value.
np.ndarray: Observed optimum of the objective.

update_mo_data(parameters: ndarray, observations: ndarray) → Tuple[float, int][source]

Get the partitioning of the observations in multi-objective optimisation.

Parameters

parametersnp.ndarray: Array of parameters.
observationsnp.ndarray: Array of observations.

Returns

Tuple[float, int]: Hypervolume and number of non-dominated points.